MacBook Pro M3 Max 128GBGünstige Wahl
128 GB Unified (+48)
B
This setup is broadly balanced for this model.21 tok/s Dekodierung
ca. $2,499 MSRP
Can Qwen 2.5 Coder 1.5B run on NVIDIA H800 80GB?
YES — Runs Great
B59Good
Estimated from fit model
Qwen 2.5 Coder 1.5B needs ~10.5 GB VRAM. NVIDIA H800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~21 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 10.5 GB, 21.0 tok/s, Runs well
10.5 GB required80.0 GB available
13% VRAM used
Fit status
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
33K
Memory
10.5 GB / 80.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.4 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 1.5B on NVIDIA H800 80GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]
The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
"""Sort a list using the quicksort algorithm.
Args:
arr: List of integers to sort.
Returns:
A new sorted list.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 21.0 tok/s | 5029 ms | 33K |
| Coding | B | Runs well | 21.0 tok/s | 9219 ms | 33K |
| Agentic Coding | B | Runs well | 21.0 tok/s | 13410 ms | 33K |
| Reasoning | B | Runs well | 21.0 tok/s | 10895 ms | 33K |
| RAG | B | Runs well | 21.0 tok/s | 16762 ms | 33K |
Quantization options
How Qwen 2.5 Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA H800 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | B58 |
Q3_K_S | 3 | 0.7 GB | Low | B58 |
NVFP4 | 4 | 0.8 GB | Medium | B58 |
Q4_K_M | 4 | 0.9 GB | Medium | B58 |
Q5_K_M | 5 | 1.1 GB | High | B58 |
Q6_K | 6 | 1.2 GB | High | B58 |
Q8_0 | 8 | 1.6 GB | Very High | B58 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | B58 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 1.5B on your machine.
Run
ollama run qwen2.5-coder:1.5bUpgrade-Optionen
Hardware, die Qwen 2.5 Coder 1.5B gut ausführt
Mac Studio M2 Ultra 128GBBestes Preis-Leistungs-Verhältnis
128 GB Unified (+48)
B
This setup is broadly balanced for this model.21 tok/s Dekodierung
ca. $3,999 MSRP
NVIDIA DGX Spark 128GBNVIDIA-Upgrade128 GB Unified (+48)
B
Adds memory headroom for longer context windows and future model growth.21 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
Frequently asked questions
Can NVIDIA H800 80GB run Qwen 2.5 Coder 1.5B?
Yes, NVIDIA H800 80GB can run Qwen 2.5 Coder 1.5B with a B grade (Runs well). Expected decode speed: 21.0 tok/s.
How much VRAM does Qwen 2.5 Coder 1.5B need?
Qwen 2.5 Coder 1.5B (1.5B parameters) requires approximately 10.5 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen 2.5 Coder 1.5B?
The recommended quantization for Qwen 2.5 Coder 1.5B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen 2.5 Coder 1.5B run at on NVIDIA H800 80GB?
On NVIDIA H800 80GB, Qwen 2.5 Coder 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.
Can NVIDIA H800 80GB run Qwen 2.5 Coder 1.5B for coding?
For coding workloads, Qwen 2.5 Coder 1.5B on NVIDIA H800 80GB receives a B grade with 21.0 tok/s and 33K context.
What context window can Qwen 2.5 Coder 1.5B use on NVIDIA H800 80GB?
On NVIDIA H800 80GB, Qwen 2.5 Coder 1.5B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-1.5b-on-h800-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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